By Li Nie, Liang Gao, Peigen Li, Xiaojuan Wang (auth.), Ying Tan, Yuhui Shi, Yi Chai, Guoyin Wang (eds.)
The two-volume set (LNCS 6728 and 6729) constitutes the refereed lawsuits of the overseas convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers awarded have been rigorously reviewed and chosen from 298 submissions. The papers are prepared in topical sections on theoretical research of swarm intelligence algorithms, particle swarm optimization, purposes of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, synthetic immune procedure, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy tools, and hybrid algorithms - for half I. issues addressed partially II are comparable to multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent platforms, info mining equipment, desktop studying tools, characteristic choice algorithms, trend acceptance tools, clever regulate, different optimization algorithms and functions, information fusion and swarm intelligence, in addition to fish university seek - foundations and applications.
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Extra info for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II
0 Research of Pareto-Based Multi-Objective Optimization 15 Table 5. The UAV received value on performing the different tasks Vehicles Received value P Task-1 Task-2 4 7 9 5 6 9 Vehicle1 Vehicle2 Vehicle3 Table 6. 2 Experimental 2. Scenario [3, 4] For the Scenario [3, 4] with three destinations, four vehicles and two tasks, the preferred value and profits are shown in the Table 7 and Table 8. The Pareto front fitness value is shown in Table 9. Table 7. 0 Table 8. The UAV received value on performing the different tasks Vehicles Vehicle1 Vehicle2 Vehicle3 Vehicle4 Received value P Task-1 Task-2 18 10 10 20 5 12 10 4 Table 9.
By considering the existing memory constraints, the determination of the complete Pareto front becomes infeasible and requires the solutions to be diverse covering its maximum possible regions. 1 Standard PSO (SPSO) A swarm in PSO consists of a number of particles. Each particle represents a potential solution of the optimization task. Each particle adjusts its velocity according to the past best position pbest and the global best position gbest in such a way that it accelerates towards positions that have had high objective (fitness) values in previous iterations.
Insert all new nondominated solution in P into A, if they are not dominated by any of the stored solutions. All dominated solutions in A by the new solution are removed from A. If A is full, the solution to be replaced is determined by the the crowding distance values of each nondominated solution in A. f. Updating of gBest. g. Calculate the new position and the new velocity. h. The procedure from c to g is repeated until the number of iterations reaches the specified number T. Then the searching is stopped and the set of A at that time is regarded as a set of optimal solutions.